System and method for valuation, acquisition and management of insurance policies

ABSTRACT

A system and method for the valuation, acquisition, and management of insurance policies whereby specific business methods, algorithms, and systems are utilized to measure specific benchmarks set by risk bearers, affiliates and clients. The benchmarks set are based on the ratios set by the risk bearer based on their specific objectives for profitability. Profitability is measured by a number of factors and ratios to include but not be limited to ROI, ROE, Combined Ratio, Expense Ratio, Loss Ratio.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application based on U.S. patentapplication Ser. No. 14/243,265 filed Apr. 2, 2014, which claimspriority to provisional U.S. patent application entitled “System andMethod for Valuation, Acquisition and Management of Insurance Policies,”filed Oct. 3, 2013, having Ser. No. 61/886,128, the entire contents ofboth are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

In underwriting insurance policies, the management of risk exposure andcost are essential to the profitability of the risk bearer. Riskbearers, or those responsible and/or impacted by the profitability of arisk bearer, can be reinsurers, primary insurance carriers, managinggeneral underwriters (“MGU's”), managing general agents (“MGA's”), thirdparty administrators (“TPA's”), program administrators, retail insuranceagents and the respective clients and/or buyers of insurance.

Profitability of risk bearers, their affiliates, and clients is based ona number of different ratios and factors. For a reinsurer or primaryinsurance carrier, for example, the main measurement is what is known asa “Combined Ratio.” It is calculated according to the National Councilof Compensation Insurance (“NCCI”) as a measure of the extent to whichpremium income covers a company's losses and expenses determined byadding together a company's loss ratio and its expense ratio. Inaddition to the unpredictability of a loss incident occurring andimpacting the loss ratio, a number of other factors hinder the abilityof risk bearers to accurately project loss and ultimately help themmanage their portfolios.

It is the current practice of risk bearers, their affiliates and clientsto establish a projection of liabilities and premiums to offset thoseliabilities at policy inception based on the characteristics of a givenexposure. Risk bearers, their affiliates and clients consider theunderlying risks such as expected payrolls, premium job classifications,state of employment, loss experience and other subjective considerationsin setting expected premiums and expenses to offset expected losses. Theprofitability of the given risk is then set by the actuarial andunderwriting units of the risk bearer for a period of twelve months.

Sophisticated models have been developed, based upon credible datapools, for use in generating these projections of liability and inprojecting return on policyholder surplus. For example, the NCCI,currently the foremost actuarial resource and ratemaking authority in 36states for workers' compensation, collects data for these 36 workers'compensation systems to better understand losses by occupation to thenset the appropriate loss costs and rates charged for those occupationsby primary insurance carriers and other risk bearers, their affiliatesand clients. Other states perform these same functions independently ofthe NCCI, such as the Worker's Compensation Insurance Rating Board(“WCIRB”) in California and the New Jersey Compensation Rating InsuranceBoard (“NJCRIB”).

Due to the manner and timing of how workers' compensation insurancecarriers report premiums, payrolls and losses to actuarial bureausthrough unit statistical cards, data is not available until at a minimum18 months after policy inception and every 12 months thereafter tounderstand the true combined ratio/profitability of the risk bearer. Thevisualization of trends and identification of outliers that impactcombined ratio/profitability has always relied on retrospective ratherthan current data sources. As a result, the earliest information withany credibility is 18 months old at best and, due to other changes inthe interim, potentially irrelevant to predicting what happens in thefuture on the line of insurance being contemplated.

Changes that occur during the twelve month policy period, such as ratechanges, addition of locations and employees, payroll increases anddecreases, job classification deviations, jurisdictional based changesor amendments and potential layoffs that may occur within the twelvemonth policy period, leads to potential pricing inadequacy because thedenominator of the combined ratio/loss ratio, known as “earnedpremiums,” is unknown. Earned premium as defined by the NCCI is theportion of the premium that represents coverage already provided and isequal to actual reported payrolls by state and class code divided by 100and then multiplied by the actual rate set by the carrier by state. As aresult, each day that an insurance policy is in force would be a day ofearned premium. This void of knowledge regarding earned premiums is aninstrumental historical problem that has plagued the industry and hasmade the actual analysis and tracking of the loss ratios and ultimatelythe profitability of risk bearers, their affiliates or clients in atimely manner virtually impossible.

Nonetheless, the practice of generating projections of losses based uponretrospective and potentially outdated exposure data is employed becausethere is currently no credible source of actual underlying premiumexposure that can be used to measure anticipated losses expense 30 to 60days prior to a policy effective/renewal date. Additionally, theinability to analyze loss expense data on a more frequent basis than thetypical standard of monthly only adds further to the speed and access todata problem. Because of the foregoing limitations on available premiumexposure data, the profitability of an insurance program can also onlybeen measured retrospectively by the bearer(s) of risk for that giveninsurance program at minimum 18 months in arrears of the programinception.

The actuarial models that exist to better understand projectedprofitability stop at the establishment of loss selection and do notrevisit profitability for at least 18 months thereafter. Actuarialscience sets expected profitability by pricing the premiums of aninsurance policy at a level where it is able to make the targetedprofitability projections within a reasonable amount of certainty as setby the Chief Actuarial Officer, Chief Underwriting Officer or otherinsurance professionals that have been given the authority to bind therisk bearer, their affiliates and clients to the potential liabilitiesof a twelve month policy.

The actuarial fellow that sets initial guidance on an insurancecarrier's portfolio of business or a specific account has nothing thatis credible to understand profitability outside of expected losses untila premium audit is performed. The denominator known as earned premiumsis not known. Premium audits are always performed after the policyexpiration and are typically completed 6 months after the policy'sexpiration.

It is often the situation that existing projection models are inadequatefor managing exposure or profitability for an individual company. By wayof example, Professional Employer Organization's (“PEO's”) which caneffectively deliver products and services to a business of any size,historically have been the most attractive to small business owners.This has been the case because small business owners are more oftenunable to provide full time employees the same benefits to deal withimportant aspects of being an employer such as human resources, employerand employee compliance (ERISA, COBRA, FMLA etc), W-2 payrolladministration and reporting, Federal and State payroll tax reporting,and, importantly, the procurement of employee related insurance plans(workers' compensation, employment practices liability, health,disability, life, 401K etc). With respect to workers' compensationinsurance, the vast majority of PEO client companies provided suchinsurance are small companies that generate limited premiums and haverelatively limited numbers of claims.

Because credible predictions of the future expectation of claims andassociated loss expenses can only be obtained through the analysis of alarger data set of claims than what can be provided by smalleremployers, i.e., hundreds to thousands of times the number of claimsexperienced by individual PEO clients, the ability to create a crediblepricing model for a workers' compensation policy for the average PEOclient company does not exist considering the lack of credibility ofavailable data on an individual client basis. Thus, the way that atypical PEO client company is being priced and underwritten by theinsurance marketplace is deficient and ultimately can and often doesimpact the profitability of risk bearers, their affiliates and clients.

As a result of the foregoing, the issuance of policies to PEO's haveoften been based on composite rates as a function of the establishedrating basis versus premium and understated ultimate expected losses andtherefore the collateral needed to offset them. Because the same limitedclient data is used to evaluate the profitability of PEO policies, thesepolicies have also suffered insufficient rate setting and inadequatesupervision of premium growth. There have also been insufficientcontrols and direction to ensure that the product provided to the PEOwas beneficial to all involved in the transaction. Whileadministratively easier, this methodology has created the inability forthe risk bearer, their affiliates and clients to know the underlyingpremium base offsetting expected losses.

What is needed therefore is a system and apparatus for allowing riskbearers, their affiliates and clients in an insurance environment, theability to more accurately forecast and manage risk exposure, theability to better set rates and pricing for the acquisition of an asset,and to continually and consistently measure the profitability of theasset as set by the hound insurance policy in a timely real-time basis.

SUMMARY OF THE INVENTION

In one aspect, the present invention provides a system and method forestablishing the valuation of an asset, pricing the asset, acquiring theasset, and then measuring the profitability of that asset based on theestablished benchmarks for measurement as set by the risk bearer itsaffiliates and clients on a real time basis.

In another aspect of the present invention, a method of managingprofitability of an insurance policy provided to a first client of aprofessional employer organization (PEO) is provided. In this methoddata relating to a PEO client is stored to a database, wherein the PEOclient data includes employee class codes and employee geographiclocation for employees covered by the insurance policy and wherein thePEO client data is associated in the database with the Federal EmployerIdentification Number (FEIN) of the PEO client. In this method real-timepayroll data is obtained from the PEO for the PEO client wherein thereal-time payroll data is associated with the FEIN of the PEO client.Also in this method, real-time earned premium data is calculated for theinsurance policy of the PEO client.

In yet another aspect of the present invention, a computer system formanaging profitability of an insurance policy provided to a first clientof a professional employer organization (PEO) is provided. This systemincludes a server for storing data relating to a PEO client to adatabase, wherein the PEO client data includes employee class codes andemployee geographic location for employees covered by the insurancepolicy and wherein the PEO client data is associated in the databasewith the Federal Employer Identification Number (FEIN) of the PEOclient. The server of this system obtains real-time payroll data from aserver associated with a PEO for the PEO client wherein the real-timepayroll data is associated with the FEIN of the PEO client. In thissystem the server calculates real-time earned premium data for theinsurance policy of the PEO client.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of facilitating an understanding of the subject mattersought to be protected, there are illustrated in the accompanyingdrawings embodiments thereof, from an inspection of which, whenconsidered in connection with the following description, the subjectmatter sought to be protected, its construction and operation, and manyof its advantages should be readily understood and appreciated.

FIG. 1 is a system resource diagram depicting the resources utilized ina exemplary embodiment of the present invention;

FIG. 2 is a flowchart of a process performed by the system of FIG. 1;

FIG. 3 is a flowchart of a process performed by the system of FIG. 1.

FIG. 4 is a flowchart of a process performed by the system of FIG. 1.

FIG. 5 is a screenshot of a visualization tool generated and displayedby the system of the FIG. 1.

FIG. 6 is a screenshot of a visualization tool generated and displayedby the system of the FIG. 1.

FIG. 7 is a screenshot of a visualization tool generated and displayedby the system of the FIG. 1.

FIG. 8 is a screenshot of a visualization tool generated and displayedby the system of the FIG. 1.

FIG. 9 is a screenshot of a visualization tool generated and displayedby the system of the FIG. 1.

FIG. 10 is a screenshot of a visualization tool generated and displayedby the system of the FIG. 1.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Turning now to the drawings, and more particularly, to FIG. 1 thereof,there is depicted an overview of the system 100 of an exemplaryembodiment of the present invention. The system 100 includes a server102 operated by an entity tasked with evaluating insurance policies,claims, payroll, and risk data, as well as collecting data frominsurance regulatory Bureaus such as the NCCI, and any other data poolsdeemed meaningful to the risk bearer, their affiliates and clients. Thisentity may also be tasked with evaluating profitability of policies byidentifying and analyzing areas of rate inadequacy, loss trending, andjurisdictional and geographic factors that influence the cost to issuepolicies and pay for losses that arise as a result of a filed claimagainst the policy. The entity operating the server 102 can be a programmanager performing the services of underwriting, collecting premiums,managing agents and processing audits for an insurance carrier. Server102, as well as other servers described herein, include one or moreprocessors, input and output devices for receiving and transmitting dataand internal and/or external data storage devices. In addition, one ormore visual display devices, including video monitors, projectors, touchscreen devices and printers, can be employed in the system hereindescribed.

Data is collected by the system 100 and integrated into server 102 usinga number of methods. In an exemplary embodiment the data includes theuse of a unique identifier, the Federal Employer Identification Number(“FEIN”), which allows all data associated with a particular client orasset to be attached, found, culled, and tracked by that specificidentifier. Thus, all policy, payroll, claims, bureau, and other data aswarranted and deemed meaningful by actuarial science is attached to theFEIN. The FEIN is a ubiquitous data point for the payroll, billing,policy, and claims systems and is the unique policyholder identifier.Thus, the system can use the FEIN to draw together both past and presentdata relating to a potential business asset of the risk bearer, theiraffiliates and clients from the various sources in managingprofitability. As a result, references herein to an asset, policy,client or FEIN can be interchanged.

In the context of worker's compensation policies issued to ProfessionalEmployer Organizations, or PEOs, Server 102 gathers policy, payroll,bureau and claims data, for individual PEO client company's workerscompensation policies from server 104 which can be maintained by aninsurance carrier or third party that is contracted to service thepolicy. In alternate embodiments, server 102 and 104 can be maintainedby the same entity and the hardware and software of these servers can becombined or distributed as appropriate for system resource managementpurposes.

In an exemplary embodiment, the system 100 can be utilized to automatethe processes of valuing the risk for an asset or policy prior tobinding; determining appropriate pricing for the policy; andbenchmarking and managing profitability following issuance of thepolicy. Exemplary processes for performing each of these functions areprovided below.

As depicted in FIG. 2, an exemplary process for valuing risk of arequested policy is provided. Data is received at step 200 and theninput 202 to the processor 102 of FIG. 1. It should be understood thatthe data received at step 200 can be provided through a physicalinsurance application form and entered into processor 102 by means of aninput device, such as a scanner or keyboard, or transmitted to thesystem by electronic means. The data provided includes information suchas that provided via standard insurance application forms such as thoseprovided by ACORD, historical loss information as provided by bothcurrent and past risk bearers, and other pertinent data necessary to thevaluation process. In the context of worker's compensation, for example,this would include such information as the type of business the asset isin, what the employees of the asset perform for the business on a dailybasis (known as the class code and as defined by the applicablebureaus), the locations where the asset operates, data associated withthe specific locations of the asset, the number of employees andassociated payrolls on a per class code and jurisdiction basis, salesand revenue data, and importantly, the FEIN. Information received atstep 200 would also include payroll data and insurance class codes foreach employee.

Following input of this data a feature of the present system and processis to generate a composite risk tolerance valuation score for an assetapplying for a policy 204. The composite score can be used to determinewhether a policy falls within a risk bearers risk profile and thusshould be underwritten by a particular risk bearer, and allows for thesystem to select the appropriate risk bearer based on the scores andfactors processed through the system. The composite score is derivedfrom a computed weighted severity score 206, a computed weightedfrequency score 208 and a computed weighted jurisdictional score 210.The method of computing each of the weighted scores and composite scorewill now be described.

In an exemplary embodiment, the weighted severity score is a measure ofthe of the potential of an asset to produce severity based losses whichspecifically effects the risk bearer's, their affiliate's and client'sprofitability measures. In an exemplary embodiment, and utilizingworkers compensation as an example, the severity of risk for eachemployee to be covered by a policy is computed using the AM Best HazardIndex 1-10, the NCCI Hazard Grade A-G and a Propensity for Loss inExcess of $250,000 calculated using an itemization of all losses in thesystem between $250,000 to $10,000,000 in order to measure propensity ofloss. A suitable starting point for such calculations would be theReveal™ Excess Pricing from the Guy Carpenter company.

The AM Best Hazard Index was developed to identify the relative degreeof risk for insurance coverages in each classification. For each line ofinsurance the number given is an indication of hazard (Low 1-3, Medium4-6, High 7-9, and Very High 10). The numbers are based on a review ofresearch material AM Best has collected, the opinions of reviewers whohave read and commented on the reports in draft stage, the opinions ofAM Best's in-house technical consultants and a review of similar orrelated classifications. In computing the weighted severity score andcomposite score, the severity scoring based on the AM Best Hazard indexis scaled to 100% by dividing 100% by 10 and multiplying by theindication of hazard number. In an exemplary embodiment, scorings up to59% based on the AM Best Hazard Index are considered desireable, 60-80%are considered acceptable and 90-100 undesirable.

Similarly, NCCI assigns each class code to a Hazard Group. Hazard groupscapture the variation in large loss potential among class codes. NCCIcurrently categorizes class codes into seven hazards groups labeled A-G.Class codes in Hazard Group A have the least likelihood of large lossand class codes in Hazard Group G have the greatest likelihood of alarge loss. In order to compute the weighted severity score andcomposite score, the severity scoring based on the NCCI Hazard Grade isscaled to 100% by dividing 100% by 7 and multiplying by 1 for an Ascore, 2 for a B score, 3 for a C score, and so on up to 7 for a Gscore. In an exemplary embodiment, scorings up to 57% are considereddesireable, 58-78% are considered acceptable and 79-100% are consideredundesirable.

Propensity for Excess Loss is a calculation based on the NCCI Hazardgrade that determines the percentage of expected loss in a class codecompared to the state's average loss between $250,000 and $10,000,000.The propensity score is determined by taking the median average of lostdollars by state and dividing that by the individual class code lossesand is provided on a scale of −50% to +50%. In an exemplary embodiment,scorings up to −5% are considered desireable, −5% to +5% are consideredacceptable and scores above +5% are considered undesirable. In order tocompute the weighted severity score and composite score, the PropensityFor Loss in Excess of $250,000 is scaled to 100% by adding 50% to thePropensity for Loss score such that a −50% Propensity for Loss scoreequates to 0% and a +50% Propensity for Loss score equates to 100%.

The weighted severity score is then computed as the average over allemployees to be covered by the policy of the sum of 30% of the AM Bestseverity score, 30% of the NCCI Hazard Grade score and 40% of thescoring of propensity for excess loss over $250,000. As can be readilyseen, the relative desirability or undesirability of a policy can alsobe determined from the relative weightings.

In an exemplary embodiment the weighted frequency score is computedusing “Bureau Loss Ratio” data and “Bureau Claims Frequency” data.Bureau Loss Ratio data are the 5-year loss ratios from NCCI and theindependent bureaus of Wisconsin, Pennsylvania, New Jersey, Minnesota,Michigan, Massachusetts, Delaware, New York, and California by code andjurisdiction. Currently, most data ranges from 2004-2010 with $12.4Trillion in Payroll and $291 Billion in on-level manual premium. Inorder to compute the weighted frequency score and composite score inthis exemplary embodiment, the Bureau Loss Ratio is capped at 100%,i.e., loss ratios above 100% are treated as being 100%. In an exemplaryembodiment, Bureau Loss Ratios up to 65% are considered desireable,65-80% acceptable and over 80% undesireable. In order to scale the lossratio to 100% for purpose of the weighted frequency score and compositescore, the loss ratio is inverted such that a 100% loss ratio is equatedto 0% and a 0% loss ratio is equated to 100%.

The Bureau Claims Frequency is a calculation of the claims per $1Million in Payroll over a 5-year period as measured by NCCI and theindependent bureaus of Wisconsin, Pennsylvania, New Jersey, Minnesota,Michigan, Massachusetts, Delaware, New York, and California by code andjurisdiction. Most data ranges from 2003-2009 with $12.4 Trillion inPayroll and $291 Billion in on-level manual premium. In order to computethe weighted frequency score and composite score in this exemplaryembodiment, the Bureau Claims Frequency is capped at 9 claims per $1Million, i.e., claims in excess of 9 per $1 Million are treated as 9claims per $1 Million. In an exemplary embodiment, Bureau Loss Ratios upto 2 are considered desireable, 3-4 acceptable and over 4 undesireable.The Bureau Claims Frequency data is then scaled to 100%, for purposes ofcalculating weighted frequency score and composite score using theformula 100% minus the product of 100% divided by 9 multiplied by theBureau Loss Claim number. For purposes of clarity, a Bureau Loss Claimnumber of 2 would be scaled to 77.8%, a Bureau Loss Claim number of 6would be scaled to 33.3%, and a Bureau Loss Claim number of 9 or abovewould be scaled to 100%.

The weighted frequency score is then computed as the sum of 50% of theBureau Loss Ratio score and 50% of the Bureau Claims Frequency Scoreaveraged over all of the employees to be covered by a policy.

In an exemplary embodiment, the weighted jurisdictional score is a scaleof 1 to 5 that the insurance carrier assigns to a state based on thedesirability to insure in that state. Criteria used in determiningranking can include favorability of workers' compensation law, ease ofadjusting, litigation rates, and carrier profitability. In order tocompute the composite score, the jurisdictional score is scaled to 100%by dividing 100 by 5 and multiplying by the assigned state score. In anexemplary embodiment, jurisdictional scores up to 30% are desireable,scores over 30% and up to 40% are acceptable and scores over 40% areconsidered undesireable. The weighted jurisdictional score is theaverage of the jurisdictional scores for all employees to be covered bya policy.

In an exemplary embodiment, the composite score is then computed 210 asthe sum of 30% of the weighted severity score, 30% of the weightedfrequency score and 40% of the weighted jurisdictional score. In anexemplary embodiment, composite scores up to 40% are consideredundesireable and thus, policies having such scores can be rejected 214.Policies having composite scores above 60% are considered desireable andthus, a decision to bind such policy can be made and the policyforwarded on for pricing 216. For policies having composite scoresbetween 40% and 60% these are considered acceptable in an exemplaryembodiment and the decision to reject 214 or price 216 can then be madebased upon additional objective factors such as prior claims history forthe client, premium amounts and the number of existing “acceptable”policies bound by the risk bearer. As will be readily understood, usingthe system and process described herein, the determination whether tobind a policy 212 can thereby be automated.

It should be recognized that the weighting of each of the inputs to thecomposite score can be adjusted based upon such factors as the nature ofinsurance to be provided and the risk profile of an individual riskbearer. In addition, additional benchmarks that can be adjusted toattain desired profitability measures using the system and methodsdescribed herein include desired return on investment, desired lossratio, desired expense ratio desired return on equity and desired returnon surplus. It should also be recognized that in addition to thecomposite score, the relative desireability of a policy can be computedfrom the desirability rankings of the individual components of thecomposite score.

It should be understood that the dividing points for rating policies asundesireable, acceptable and desireable based upon the exemplarycomputations of weighted severity score, weighted frequency score,weighted jurisdictional score and composite score described above havebeen found to be particularly advantageous. Nonetheless, it is intendedthat these dividing points be adjusted such as for the purpose ofaccommodation of individual risk profiles of carriers.

Following the valuing process of FIG. 2, the process of pricing thepolicy can then be carried out by system 100 of FIG. 1 utilizing theprocess now described in connection with FIG. 3. In pricing a policy tobe bound, data relating to the policy is first received by the system300. The data received by the system can include data forwarded 216 asthe output of the valuing process of FIG. 2. Data in addition to thatprovided by the valuing process may also be obtained and used inselecting the appropriate risk bearer and then computing pricing of apolicy. Examples of such data include Insurance Rate data 302, Debit andCredit Factors 304 and Rate Basis information 306. Utilizing theforegoing data the price of a policy to be bound can be calculatedutilizing standard pricing formulas 308.

Following the pricing of a policy 308 the policy is bound and issued 310and may then be entered into a database of policies forming theportfolio of assets of a risk bearer 312, their affiliates and clients.The database of policies may be associated and integrated with theserver of the risk bearer 104 or the server of a program manager 102.Thus, FIGS. 2 and 3 outline the initial entry of asset data into thesystem, how the system evaluates the data in conjunction with a set ofprofitability benchmarks and applies actuarial analytics and predictivemodeling to the asset, and how the asset will be valued and ultimatelypriced as a result of the processing of the data associated with theasset to the established profitability benchmarks.

Following issuance of a policy, the system of FIG. 1 can be used tomanage the performance and ultimately the profitability of that policyin a manner that was not heretofore possible. In order to manageperformance and profitability, data regarding the asset is stored in adatabase 400 associated with or accessible by server 102. In anexemplary embodiment, this data can be provided and is compared andmeasured to the established benchmarks set by the risk bearer, theiraffiliates and clients. The output of the automated asset and riskvaluation model, the pricing module and additional data can also bestored in the database which is associated with server 102 or isaccessible thereby. This additional data can include, for example, dataprovided by one or more of the appropriate and governing insuranceBureau's such as NCCI 402.

Where the policy database is associated with server 102, server 102further gathers data directly from insurance clients 106, 108 and 110 ofFIG. 1, including real-time payroll data 404 as well as insurance classcodes, rates, and other applicable factors to include credits, debits,and experience modification factors, where applicable, needed toadequately price an insurance policy. In the exemplary application toworkers compensation insurance provided to PEO's, the PEO will beproviding payroll services to its clients and thus, real-time payrolldata is available to server 102 to provide perform profitabilitycalculations.

In an exemplary embodiment, additional information is also collectedfrom the clients 106-108 and 110 including by server 102, for example,employee names, locations, number of employees, and the mandatoryidentifier, the FEIN. Where the policy database is associated with theserver of the risk bearer 104, server 102 also collects this data inorder to manage the performance of the assets and the associated issuedpolicies in the portfolio and in accordance with the establishedbenchmarks of the risk bearer, their affiliates and clients.

In addition to information relating to a particular client, server 102can also draw information from additional sources 112, 114 and 116, inorder to measure and analyze both asset and overall portfolioprofitability applying the established benchmarks with the risk bearer,their affiliates and clients based on individualized risk profiles byutilizing a much broader dataset than is available from an individualclient's loss history. For example, the profitability of an individualpolicy can be evaluated against the data having the same class codes andgeographic location from the broader data set reported to actuarialbureaus.

From this evaluation it can be determined how actual profitability ismeasured utilizing current data that compares it to the established riskbearer, profitability benchmarks for that assets risk profile to includeseverity, frequency, and jurisdictional measures. As can be readilyunderstood, the ability to determine the profitability of an individualpolicy on a payroll cycle basis, and evaluate that against expectedprofitability, allows for improved management of the risk bearer's,their affiliates, and clients portfolio of assets in accordance with theestablished profitability measures in a manner much more, quickly andefficiently then current methods.

As an example to the measurement of operational profit for a riskbearer, their affiliates and clients, a primary insurance carrierutilizes the combined ratio method. The International Risk ManagementInstitute (“IRMI”) defines the combined ratio of an insurance carrier asthe sum of two ratios, the loss ratio, which is calculated by dividingincurred losses plus loss adjustment expense (LAE) by earned premiums(the calendar year loss ratio), and the other, the expense ratio, whichis calculated by dividing all other expenses by either earned premiums(i.e., trade basis or statutory basis expense ratio). When applied to acompany's overall results, the combined ratio is also referred to as thecomposite or statutory ratio. Used in both insurance and reinsurance, acombined ratio below 100 percent is indicative of an underwritingprofit, a combined ratio greater than 100 percent is indicative of anunderwriting loss and a combined ratio of exactly 100 percent isindicative that every premium dollar is being used to pay claims andcover operating costs with nothing remaining for insurer profit.Furthermore, the cost containment of all expenses in the system yields agreater return on investment (ROI) for the risk bearer, their affiliatesand clients. Such expenses include but are not limited to taxes,commissions, legal, managed care, surveillance, reinsurance charges, andinsurance charges.

A more concise definition that can be used to get to the samemeasurement is that Combined Ratio=Expenses (administrative cost,profit, reinsurance, cost to adjudicate claims, taxes, commissions andother set costs)+Expected Losses (Broken out by wage loss, medical andcost containment expenses)/Actual Earned Premiums (Premiums derived frompayrolls by workers' compensation classification code by state)+OtherIncome (i.e., investment return in reserves not yet paid). In order toestablish a return on surplus of 15% an insurance carrier needs todeliver a combined ratio less than 95%.

The total after tax return to the risk bearer (T) is equal to the sum ofinvestment return on assets (I/A) where I is the investment gain or lossand A is the total assets, multiplied by an insurance leverage factor(1+R/S)dependent on the size of reserves relative to surplus, where R isthe reserves and other liabilities (excluding equity in unearned premiumreserves) and S is the stockholders equity (capital, surplus, and equityin unearned premium reserve), plus the underwriting profit (or minus theunderwriting loss) on premiums (U/P), where U is the underwriting profitand P is the premium income, multiplied by an insurance exposure term(P/S) relating premiums to surplus.

The formula does not require a mutually exclusive choice betweeninvestment or total assets as an investment base but rather points outtheir interdependence. The formula contains a third rate of returnmeasure in the form of the U/P ratio, a familiar and traditionalbenchmark for measuring underwriting results. This is meant to show therelationship among return on investment, return on assets, and return onsales.

It can be readily understood that utilizing actual payrolls, rather thanestimated payrolls, to calculate actual premiums, rather than estimatedpremiums allow for the ability to measure actual profitability, ratherthan estimated profitability of a client policy in an extremelyefficient and timely manner when compared to current methods of havingto wait for periods of a minimum of 15 up to 18 months after a policy'sinception. These scenarios can be measured by server 102 byunderstanding actual payrolls and premiums by payroll cycle rather thanrelying on data reported to actuarial bureaus over a year earlier. Itwill also be readily recognized that using the system and method of thepresent invention will determine and provide more accurate informationregarding the profitability of an asset within the portfolio as well asthe entire portfolio of assets.

Through use of the presently described system, data can be provided torisk bearers, their affiliates and clients in ways that assist inmanaging risk such as by identifying geographic areas experiencingundesirable profitability scenarios such as high loss ratios and lowreturns on equity. Data for each client is stored in a data warehouseand linked by the FEIN. Thus, the FEIN is particularly useful in sortingand culling the data 406 to generate visualization tools 408. Thesevisualization tools are then displayed to users for managing policies.

As a result of the currency of data being utilized in the presentsystem, visualization tools can be particularly helpful in managing theperformance and profitability of a portfolio of assets. For example, asshown in FIG. 5 a geographic map of coverage areas 500 can be presentedto a user with policies represented by circles 502 colored to correspondto severity ranking according to the desirable, acceptable andundesirable paradigm selected for a risk bearer. Additionally, the sizeof the circles corresponds with the size of the asset being measured.Size can correspond to but is not limited to the size of an assetspayroll, premiums, claims costs, and employee counts.

Using such a map, risk bearers, their affiliates and clients can botheasily and quickly identify potential outliers that can impact theperformance and ultimate profitability of an asset or portfolio ofassets. Examples would be identifying where policies have potentialunacceptable severity exposures and associated costs, where they mayhave potential frequency outliers that can drive claims costs, and wherethey may have poor jurisdictional measures such as loss ratios andreturn on equity that will affect the overall profitability of the assetand portfolio, As will be readily understood, the combination ofreliance on real-time payroll, claims, and policy data to computeseverity rankings combined with the visualization of these rankingsgeographically can allow a manager to identify clumping ofunderperforming assets. Such clumping may indicate, for example, ajurisdiction to be avoided in acquiring new assets and binding newpolicies, or an area in need of mitigation or reduction efforts such asloss control, risk management, safety training, claims costs analysis,and reserve setting.

In alternative embodiments, the circles can provide links to dataidentifying the individual client associated with that circle as well asproviding links to data relating to the severity ranking or any otherdata associated with the client. The size of the red dot can also bescaled based upon the relative percentage of the current loss overprojections.

FIGS. 6-8 provide additional geographic visualization tools generatedutilizing weighted frequency scores, weighted jurisdiction scores andcomposite scores all generated utilizing real-time payroll, claims andpolicy data. As with the geographic representation of severity scores inFIG. 5, the geographic representation of frequency, jurisdiction andcomposite score data can be utilized to manage a risk bearerprofitability in real-time.

It should be readily recognized that the geographic representation ofdata in FIGS. 5-8 are exemplary and that such data may alternatively beprovided in the form of gauges, views, graphs, charts, reports, and mostimportantly alerts as deemed useful by a particular risk bearer, theiraffiliates and clients and in correlation with their specificestablished benchmarks for profitability measurement and identification.For example, as depicted in FIG. 9, a screenshot is provided of anexemplary dashboard of claim frequency data 900. As seen in FIG. 9,claims frequency data can be provided for workers compensation policiesusing current payroll data in a number of formats including a bar graph902 displaying the claim frequency of the client in a particularjurisdiction computed using current payroll data alongside the claimsfrequency as computed using the most recent Bureau data which, as notedabove, is potentially 18 months stale

Another exemplary visualization tool is presented in FIG. 10 in which isdisplayed a screenshot of a dashboard of data for an exemplary PEO. Itshould also be readily recognized that the system of FIG. 1 can be usedto provide reports and alerts to clients based upon the real-time dataprovided by clients. For example, the system may be configured to sendan alert to a program manager when the current pay-roll data provided byclients causes the workers compensation policies of 5 or more clients ina single ZIP code to become unprofitable.

It will be recognized that the processes described herein can be carriedout by means of computer programs and the programming of specific yetunlimited algorithms that are stored in the identified servers or storedin other computer readable media accessible by the identified serversand an vast number of data pools from which an unlimited number ofalgorithms can be developed and utilized for analysis and measurementpurposes. One or more programs stored on one or more servers and/orcomputer readable media can operate cooperatively to perform theprocesses described herein.

Although the system presented herein has been described in terms of aspecific number of servers and databases, it should be understood thatany number of servers and databases can be utilized in practicing thepresent system, and the systems ability to pull data from numerous datasources, cull and organize the data, then apply algorithms to allow theservers to manipulate and ultimately display the data in a very conciseformat and view is part of the system and ultimately the inventionsunique and proprietary characteristics. It is also possible to combinethe functions of various servers without departing from the spirit andscope of the present invention.

Moreover, unless specifically called out, the description of processesperformed by the system of the present invention in a particular orderis not intended to suggest that this order is required. Thus, anyreordering of the steps of the process that achieves the stated purposemay be performed and yet another unique and proprietary feature to thesystem and invention's ability to adapt to all of the ongoing changeswithin the environment that it serves.

Where the system and processes described herein have been described foruse in connection with a particular type of insurance policy this hasbeen done to provide a context for aiding in understanding those systemsand processes. It is not intended that the systems and processes belimited in their application to a particular type of insurance. Thesystem is utilized in measuring profitability factors for risk bearers,their affiliates and clients for other lines of insurance such asgeneral liability, employment practices liability, auto, property,disability, and major medical and other health insurance coverages, toname a few.

Additionally, where the system and processes described herein have beendescribed for use in connection with a particular type of industry, itshould be reasonably understood that the system can also be used inother industries such as legal, managed care, aviation, real estate, andhealthcare.

The matter set forth in the foregoing description and accompanyingdrawings is offered by way of illustration only and not as a limitation.While particular embodiments have been shown and described, it will beapparent to those skilled in the art that changes and modifications maybe made without departing from the broader aspects of applicants'contribution. The actual scope of the protection sought is intended tobe defined in the following claims when viewed in their properperspective based on the prior art.

What is claimed is:
 1. A method of managing at least one insurancepolicy associated with at least one respective professional employerorganization (PEO) client of a PEO, the method comprising: (a) storingemployee geographic location data for employees covered by the at leastone insurance policy in a database based upon a federal employeridentification number (FEIN) associated with the at least one respectivePEO client; (b) obtaining payroll data for the at least one respectivePEO client based upon the respective FEIN; (c) dynamically calculating,in real-time, earned premium data for the at least one insurance policy;(d) dynamically calculating, in real-time, a policy ranking based uponthe earned premium data; (e) dynamically calculating, in real-time, anindicator scaling factor corresponding to a size of the at least oneinsurance policy; (f) dynamically generating, in real-time, a graphicalrepresentation for display including a plurality of dynamic indicatorsassociated with the at least one insurance policy and based upon theemployee geographic location data, each dynamic indicator having atleast one indicator characteristic based upon the indicator scalingfactor and the policy ranking; and (g) dynamically updating the at leastone indicator characteristic of the at least one dynamic indicator inreal-time based upon real-time changes in the calculated real-timeearned premium data by repeating steps (b)-(f).
 2. The method of claim 1wherein the graphical representation comprises a geographic map ofpolicy coverage areas for display including the plurality of dynamicindicators at corresponding geographic locations.
 3. The method of claim1 wherein the at least one insurance policy comprises a plurality ofinsurance policies; and wherein each of the plurality of dynamicindicators is each associated with a corresponding one of the pluralityof insurance policies.
 4. The method of claim 3 wherein the policyranking corresponds to a profitability of each of the plurality ofinsurance policies.
 5. The method of claim 1 wherein the policy rankingcorresponds to a profitability for the least one insurance policy. 6.The method of claim 1 wherein the at least one PEO client comprises aplurality of PEO clients.
 7. The method of claim 6 wherein the at leastone insurance policy comprises a plurality of insurance policies; andwherein the policy ranking corresponds to a profitability of each of theplurality of insurance policies for employees of each of the pluralityof PEO clients at a same zip code.
 8. The method of claim 1 wherein theat least one visual characteristic comprises color.
 9. The method ofclaim 1 wherein the at least one visual characteristic comprises size.10. The method of claim 1 further comprising dynamically calculating, inreal-time, profitability for each employee based upon the earned premiumdata; and wherein the at least one indicator characteristic isdynamically updated in real-time based upon real-time changes in thecalculated profitability.
 11. The method of claim 1 further comprisingstoring claims data associated with the at least one insurance policy inthe database based upon the FEIN; and wherein the policy ranking isdynamically calculated based upon the claims data.
 12. The method ofclaim 1 wherein the graphical representation comprises at least one ofan icon, graph, table, and textual representation.
 13. A method ofmanaging at least one insurance policy associated with at least onerespective client of an insurance organization, the method comprising:(a) storing employee geographic location data for employees covered bythe at least one insurance policy in a database based upon a federalemployer identification number (FEIN) associated with the at least onerespective client; (b) obtaining payroll data for the at least onerespective client based upon the respective FEIN; (c) dynamicallycalculating, in real-time, earned premium data for the at least oneinsurance policy; (d) dynamically calculating, in real-time, a policyranking based upon the earned premium data; (e) dynamically calculating,in real-time, an indicator scaling factor corresponding to a size of theat least one insurance policy; (f) dynamically generating, in real-time,a graphical representation for display including a plurality of dynamicindicators associated with the at least one insurance policy and basedupon the employee geographic location data, each dynamic indicatorhaving at least one indicator characteristic based upon the indicatorscaling factor and the policy ranking; and (g) dynamically updating theat least one indicator characteristic of the at least one dynamicindicator in real-time based upon real-time changes in the calculatedreal-time earned premium data by repeating steps (b)-(f).
 14. The methodof claim 13 wherein the graphical representation comprises a geographicmap of policy coverage areas for display including the plurality ofdynamic indicators at corresponding geographic locations.
 15. The methodof claim 13 wherein the at least one insurance policy comprises aplurality of insurance policies; and wherein each of the plurality ofdynamic indicators is associated with a corresponding one of theplurality of insurance policies.
 16. A system for managing at least oneinsurance policy associated with at least one respective professionalemployer organization (PEO) client of a PEO, the system comprising: amemory; and a processor cooperating with the memory and configured to(a) store employee geographic location data for employees covered by theat least one insurance policy in a database in the memory based upon afederal employer identification number (FEIN) associated with the atleast one respective PEO client, (b) obtain payroll data for the atleast one respective PEO client based upon the respective FEIN, (c)dynamically calculate, in real-time, earned premium data for the atleast one insurance policy, (d) dynamically calculate, in real-time, apolicy ranking based upon the earned premium data, (e) dynamicallycalculate, in real-time, an indicator scaling factor corresponding to asize of the at least one insurance policy, (f) dynamically generate, inreal-time, a graphical representation for display including a pluralityof dynamic indicators associated with the at least one insurance policyand based upon the employee geographic location data, each dynamicindicator having at least one indicator characteristic based upon theindicator scaling factor and the policy ranking, and (g) dynamicallyupdate the at least one indicator characteristic of the at least onedynamic indicator in real-time based upon real-time changes in thecalculated real-time earned premium data by repeating steps (b)-(f). 17.The system of claim 16 wherein the graphical representation comprises ageographic map of policy coverage areas for display including theplurality of dynamic indicators at corresponding geographic locations.18. The system of claim 16 wherein the at least one insurance policycomprises a plurality of insurance policies; and wherein each of theplurality of dynamic indicators is each associated with a correspondingone of the plurality of insurance policies.
 19. The system of claim 16wherein the policy ranking corresponds to a profitability for the leastone insurance policy.
 20. The system of claim 16 wherein the at leastone PEO client comprises a plurality of PEO clients.
 21. The system ofclaim 20 wherein the at least one insurance policy comprises a pluralityof insurance policies; and wherein the policy ranking corresponds to aprofitability of each of the plurality of insurance policies foremployees of each of the plurality of PEO clients at a same zip code.22. The system of claim 16 wherein the at least one visualcharacteristic comprises at least one of color and size.
 23. The systemof claim 16 wherein the processor is configured to dynamicallycalculate, in real-time, profitability for each employee based upon theearned premium data; and wherein the processor is configured todynamically update the at least one indicator characteristic inreal-time based upon real-time changes in the calculated profitability.24. The system of claim 16 wherein the processor is configured to storeclaims data associated with the at least one insurance policy in thedatabase based upon the FEIN; and wherein the policy ranking isdynamically calculated based upon the claims data.
 25. A non-transitorycomputer readable medium for managing at least one insurance policyassociated with at least one respective professional employerorganization (PEO) client of a PEO, the non-transitory computer readablemedium comprising computer executable instructions that when executed bya processor cause the processor to perform operations, the operationscomprising: (a) storing employee geographic location data for employeescovered by the at least one insurance policy in a database based upon afederal employer identification number (FEIN) associated with the atleast one respective PEO client; (b) obtaining payroll data for the atleast one respective PEO client based upon the respective FEIN; (c)dynamically calculating, in real-time, earned premium data for the atleast one insurance policy; (d) dynamically calculating, in real-time, apolicy ranking based upon the earned premium data; (e) dynamicallycalculating, in real-time, an indicator scaling factor corresponding toa size of the at least one insurance policy; (f) dynamically generating,in real-time, a graphical representation for display including aplurality of dynamic indicators associated with the at least oneinsurance policy and based upon the employee geographic location data,each dynamic indicator having at least one indicator characteristicbased upon the indicator scaling factor and the policy ranking; and (g)dynamically updating the at least one indicator characteristic of the atleast one dynamic indicator in real-time based upon real-time changes inthe calculated real-time earned premium data by repeating steps (b)-(f).26. The non-transitory computer readable medium of claim 25 wherein thegraphical representation comprises a geographic map of policy coverageareas for display including the plurality of dynamic indicators atcorresponding geographic locations.
 27. The non-transitory computerreadable medium of claim 25 wherein the at least one insurance policycomprises a plurality of insurance policies; and wherein each of theplurality of dynamic indicators is each associated with a correspondingone of the plurality of insurance policies.
 28. The non-transitorycomputer readable medium of claim 25 wherein the at least one PEO clientcomprises a plurality of PEO clients; wherein the at least one insurancepolicy comprises a plurality of insurance policies; and wherein thepolicy ranking corresponds to a profitability of each of the pluralityof insurance policies for employees of each of the plurality of PEOclients at a same zip code.
 29. The non-transitory computer readablemedium of claim 25 wherein the at least one visual characteristiccomprises at least one of color and size.
 30. The non-transitorycomputer readable medium of claim 25 wherein the operations furthercomprise dynamically calculating, in real-time, profitability for eachemployee based upon the earned premium data; and wherein the at leastone indicator characteristic is dynamically updated in real-time basedupon real-time changes in the calculated profitability.
 31. Thenon-transitory computer readable medium of claim 25 wherein theoperations further comprise storing claims data associated with the atleast one insurance policy in the database based upon the FEIN; andwherein the policy ranking is dynamically calculated based upon theclaims data.